Over the past several decades, travel “diaries” have persisted as the main data collection tool for understanding and forecasting travel behaviour. Along the way, many enhancements, supplements and alternative delivery mechanisms have been developed to increase their accuracy, reduce respondent burden, and extend the types of information gathered. This includes the move to more activity-based and time-use diary methods. Increasing attention is also being paid to issues of data quality, standards, and non-response. Wholly new techniques have also emerged that incorporate or extend diaries in key ways to capture people's motives, decisions processes, and future behavioural responses. These include stated preference/adaptation surveys, gaming and simulation, panel surveys, cognition experiments such as “think aloud” protocols, activity scheduling process surveys, and a wide variety of qualitative approaches. Increasingly, new technologies such as computers, the Internet, and passive tracking technologies are also being used to support these efforts.
In particular, Global Positioning Systems (GPS) are receiving considerable attention and experimentation as a means to supplement and potentially replace travel diary methods. Most off-the-shelf GPS devices are capable of providing location accuracy (longitude, latitude, and altitude) within 5 to 10 meter accuracy. After a warm-up of 30-90 seconds, such devices can provide such information on a second-by-second basis if desired. When combined with a data logger such as a hand-held computer, GPS can passively provide a highly accurate trace of personal or vehicular movements over long periods of time.
Geographic Information Systems (GIS) are an ideal tool for storing, processing, and visualizing this data. Recent research has shown that it is possible through post-processing to accurately determine the vast majority of travel routes, traffic flows, trip start/end times, and trip stops (or activity locations) from GPS data.
For example, Bullock et al. utilized a combination of automated rules and by-hand techniques to successfully detect 91% of trips. (See Bullock, P. J., P. R. Stopher, and F. N. F. Horst. Conducting a GPS Survey with Time-Use Diary. Presented at The 82nd Annual Meeting of the Transportation Research Board, Washington, D.C., 2003.) A basic rule for identifying trip ends was first used, consisting of at least 120 seconds worth of stationary GPS points. Additional rules dealt with situations such as signal loss or vehicle reversals.
Doherty et al. utilized GIS to develop algorithms that detected trip routes by matching GPS points to the nearest link using a “buffering” technique, and identified trip-ends (or “activity” nodes) based on the clustering of GPS points, leading to over 90% accuracy without further by-hand processing. (See Doherty, S. T., N. Noël, M. Lee-Gosselin, C. Sirois, and M. Ueno. Moving beyond observed outcomes: integrating Global Positioning Systems and interactive computer-based travel behaviour surveys, In Transportation Research Circular: Personal Travel: The Long and Short of It (No. E-C026). Transportation Research Board, National Research Council, Washington, D.C., Washington, D.C., 2000, pp. 449-466.) When compared to self-reports, GPS has been shown to increase trip detection by 20-30%, especially for shorter more discretionary trips.
In most of these applications, GPS has been used largely to detect observed attributes of vehicular trips, including start, end times, routes, and trip ends. For example, U.S. Pat. No. 6,961,658 to Ohler discloses a vehicle navigation system that records information regarding trips regularly taken by a vehicle and populates a database with records of routine trip information. This trip information is then used to provide for the user traffic conditions associated with the routine trip route, among other things.
As a further example, U.S. Pat. No. 6,952,645 to Jones discloses a system for monitoring the travel of vehicles in response to requests from users in remote locations. The system comprises a data manager that records location data and vehicle information in response to requests, and then compares the data and information to determine whether the vehicle is a predetermined proximity from the location identified by the location data. The data manager transmits a message to the user if the vehicle is a predetermined proximity from the location.
However, in the context of emerging travel behaviour and activity-based approaches, these vehicle-focussed systems represent only a subset of the types of information commonly sought in diary-based surveys, albeit the most difficult for people to recall in most cases. Person-based GPS tracking holds the potential for extending the extent of detection to include trips by other modes (walk, bike, bus, train, etc.), and more complex activity patterns. Overall, GPS appears to offer unprecedented potential to improve the data quality and extent of travel surveys, and thus inherently reduces respondent burden.
As an alternative to GPS, Asakura and Hato showed how cellular phones can be used to track people at 2-minute intervals to within 20-150 meter accuracy. (See Asakura, Y. and E. Hato. Tracking survey for individual travel behaviour using mobile communication instruments. Transportation Research Part C, Vol. 12, No. 3-4, 2004, pp. 273-291.) Instead of satellites, location is estimated using the antennas of service providers located about every 100 meters in Osaka, Japan. In a test, Asakura and Hato use a basic “move or stay” rule to post-process such data to detect trips by start and end time, and demonstrate with examples how these compare to a trip diary for the same person. They conclude that such a system holds potential for monitoring travel behaviour in a real world environments, but that much further research is needed. In particular, they suggest further development of trip, mode and route detection algorithms based on statistical analysis of the observed location data rather than ad-hoc rules. They also suggest that future tracking surveys incorporate a cell-phone based questionnaire to capture undetectable trip attributes such as trip purpose.
In general, the growing emergence of location-enabled cellular phones is posing new opportunities and challenges for the development of Location-Based Services (LBS). For consumers, current LBS are largely limited to navigational or tracking systems that can display a persons' current location on a map and assist with way-finding or the identification of points-of-interest. These are widely available and, for example, include the recent SEEK & FIND™ and GOTRAX™ services offered by Bell Canada.
Despite all these key advances, it is largely recognized that GPS or any other tracking technologies will never provide for the ability to completely replicate a person's activity-travel patterns, nor capture all the typical elements included in activity-trip diaries. Well known signal outages, positional inaccuracies, cold-start issues, and other technical problems prevent 100% tracing. These problems are even more acute for person-based tracking, as people enter buildings/tunnels frequently and loose GPS or cellular signals or just get their body in the way of the antennae.
These problems were recognized in U.S. Pat. No. 6,898,518 to Padmanabhan, which teaches a non-automated method of identifying a human user's location by interpreting the user's visual cues, such as landmarks. Although an interesting approach, the method is limited because it relies on continuous user input and is therefore not capable of producing detailed and accurate location data that can be produced by a passive tracing means.
Even if the problems associated with automated tracing techniques are overcome through improved technologies, it is likely that no algorithm will ever be able to 100% detect all the complexities of the activities and trips made by people; e.g., multi-stop activities, short drop-off activities, and other random-like patterns are likely to remain difficult to detect automatically.
Perhaps more importantly, there are activity-trip diary elements that are difficult if not impossible to trace with GPS, including involved persons (unless perhaps, everyone was GPS traced), activity types (or trip purposes), and the underlying motives and decision processes that underlie observed patterns captured by diaries. This includes the Who, What, When, Why, and How, not just the Where—five additional elements that could vastly expand the value of such a system for a very wide variety of applications. What is a person doing? (Stationary activity, exercising, travelling, etc.) How did they get there? (By car, walk, bike, etc.) When and how long have they been doing it? With whom? Why are they doing it? (Shopping, working, routine, etc.) Even more valuable is the tracking and display of these answers for an entire day or week. Although the demand for this information has been ever increasing in the quest to better understand and forecast travel behaviour, there are few solutions aimed at processing person-based data to provide a more detailed accounting of a person's behaviour beyond just location.
However, some are going beyond the detection of observable patterns based on GPS, and have demonstrated potential for automated detection of attributes such as trip purpose as inferred from underlying land-use data and even underlying activity scheduling decisions such as activity-travel modifications and impulsive decisions. As a result, a hybrid class of location data supported diary surveys is emerging that combine passive tracing for the detection of a portion of peoples' activity-trip pattern, followed by an active and explicit attempt to prompt users to recall undetected elements and supplemental information. This hybrid design can be termed a “Prompted Recall” survey. Such surveys recognize that, rather than relying on time-consuming manual inspection and interpretation of GPS- or cellular-traced data to determine a person's activity-travel pattern (including interpreting gaps and detecting activities and trips), the real “experts” on the patterns is approached to do so: the persons themselves.
What is needed therefore is a method, system and computer program for monitoring human activity utilizing location data that overcomes the limitations and drawbacks of the prior art.